Constrained correlation functions

TL;DR

This paper derives inequality constraints on correlation functions of homogeneous and isotropic random fields, demonstrating their significant impact on the validity of Gaussian likelihood models in cosmological data analysis.

Contribution

It provides explicit bounds on correlation coefficients for random fields and highlights the importance of these constraints in statistical modeling, especially in cosmology.

Findings

Correlation functions must satisfy derived inequality constraints.

Gaussian likelihood models can extend into forbidden correlation regions.

Numerical experiments show Gaussian likelihood assumptions may be invalid for correlation data.

Abstract

We show that correlation functions have to satisfy contraint relations, owing to the non-negativity of the power spectrum of the underlying random process. Specifically, for any statistically homogeneous and (for more than one spatial dimension) isotropic random field with correlation function $\xi(x)$, we derive inequalities for the correlation coefficients $r_n\equiv \xi(n x)/\xi(0)$ (for integer $n$) of the form $r_{n{\rm l}}\le r_n\le r_{n{\rm u}}$, where the lower and upper bounds on $r_n$ depend on the $r_j$, with $j<n$. Explicit expressions for the bounds are obtained for arbitrary $n$. These constraint equations very significantly limit the set of possible correlation functions. For one particular example of a fiducial cosmic shear survey, we show that the Gaussian likelihood ellipsoid has a significant spill-over into the forbidden region of correlation functions, rendering the resulting best-fitting model parameters and their error region questionable, and indicating the need for a better description of the likelihood function. We conduct some simple numerical experiments which explicitly demonstrate the failure of a Gaussian description for the likelihood of $\xi$. Instead, the shape of the likelihood function of the correlation coefficients appears to follow approximately that of the shape of the bounds on the $r_n$, even if the Gaussian ellipsoid lies well within the allowed region. For more than one spatial dimension of the random field, the explicit expressions of the bounds on the $r_n$ are not optimal. We outline a geometrical method how tighter bounds may be obtained in principle. We illustrate this method for a few simple cases; a more general treatment awaits future work.